Journal of Geographic Information and Decision Analysis, vol.1, no.1, pp. 9-24 
Object-oriented Simulation and Evaluation of River Basin Operations

René F. Reitsma 
CADSWES/CEAE, University of Colorado, Campus Box 421, Boulder, CO 80309-0421, USA 

John C. Carron 
CADSWES/CEAE, University of Colorado, Campus Box 421, Boulder, CO 80309-0421, USA  

ABSTRACT  A computational framework for the integration of physical process modeling and multicriterion evaluation of river basin operations is presented. Despite a rich tradition in water resources multiobjective programming, most real-world water resources planning relies on simulation followed by ex-post multicriterion evaluation. In this paper we present a computational framework for the integration of modeling and ex-post evaluation by means of object-orientation. Advantages are a simpler, less ambiguous software data model; concurrent computation of physical and multicriterion aspects of a natural resource; efficient locale-specific evaluations; and interactive, interest-specific definitions of evaluatory views. The concepts are tested in the context of simulating and evaluating Colorado River operations.
KEYWORDS: environmental planning, multiobjective planning, multiobjective evaluation, object-orientation, interest-specific evaluation, river basin management, simulation, data model, decision support systems.

Acknowledgments This research was sponsored by the Colorado Advanced Software Institute (CASI),  grant number 93-0003 and IBM Corporation, Boulder, Colorado.


1. Introduction: The DSS Data Model for Environmental Planning
Environmental resource planning problems comprise a subset of the more general class of public policy and resource allocation problems. As such, they are complex in that they contain technical as well as organizational and sociopolitical components which maintain complicated interrelationships (Brenner 1973; Cohon 1978; Kaufman and Duncan 1990; Glasbergen 1994; Purdy and Gray 1994). This complexity renders many environmental management problems ill-structured or ill-defined. Bosman (1983) defines ill-structured problems as those for which one of the following conditions is not met:         Since the second and third criterion are necessary conditions for the application of multiobjective programming (Cohon 1978; Rios 1994), other, less structured approaches for the resolution of this class of problems have been developed. One such an approach is that of decision support systems (DSS). Proponents of these systems (Loucks et al. 1985; Fedra et al. 1986, 1993; McLean and Sol 1986; Guariso and Werthner 1989; Sprague and Watson 1993; Finlay 1994) propose that DSS offer multiple representations of the decision problem, combined with facilities that allow interactive assessment of the various aspects of the problem through different models, data visualizations, multicriterion evaluations and reports. The paradigm is simple, yet elegant. Rather than attempting to a priori resolve and formalize the problem in its entirety, only easy-to-formalize components are represented and augmented with software tools that allow users to flexibly navigate the decision space. For environmental DSS, this usually results in systems that are constructed around three main components (Figure 1):  Figure 1 Environmental Decision Support System
Figure 1  Environmntal Decision Support System

      The remainder of this paper consists of three sections. First, we explore some of the problems associated with the above DSS architecture. Next, we introduce an alternative, object-oriented architecture and argue that this architecture resolves some of these problems. Finally, we illustrate the use of the approach by means of an application of object-oriented multicriterion evaluation of streamflow regimes in the Colorado River.

2. Problems of the DSS Data Model

Although the conventional DSS architecture provides a viable basis for the formulation of the functional components of an environmental DSS, it is often problematic from a computational and implementational point-of-view in that the different components have traditionally required different software data models. For instance, state information is often represented using relational and spatial (GIS) data models for attribute and spatial data respectively, whereas process information is represented in simulation models. Evaluation tools, in turn, are often implemented as specialized software containing highly structured, formal techniques such as multicriterion evaluation models (Nijkamp et al. 1990; Korhonen et al. 1992), multiattribute preference models (Timmermans 1986; Timmermans and van der Heijden 1987), or more ad hoc techniques such as data visualization or report generation. As a result, the traditional architecture of environmental DSS is a collection of more or less independent software components and data models, integrated through a complex infrastructure of data pipes and channels, files and memory mappings (Reitsma 1990; Fedra 1991). The disadvantages of this kind of "dedicated" representation are obvious: complex data logistics; highly specialized, nonmodular implementations; poor technology transfer; and high maintenance and extension costs (Dames and Moore 1993).

2.1. Locale-specific Evaluations

Associated with these logistic difficulties are other, more conceptual problems. One of those concerns the problem of place- or locale-specific plan evaluation. For instance, fish habitat studies of the Colorado River have shown different empirical relationships between streamflow and habitat suitability for different sections of the river, for the same fish species. Likewise, a utility function for white water rafting based on streamflow will have to vary by locale, because only through the interaction between flow and channel morphology can a value for rafting utility be inferred. For example, equal rates of flow in the lower Colorado and in one of its headwater tributaries might result in optimal conditions in one locale, and hazardous conditions in the other. The above DSS architecture, where plan evaluation is implemented as a separate set of software applications, makes it hard to conduct this type of analysis.

2.2. Ad hoc Plan Evaluation

Formal ex-post plan evaluation methods such as multicriterion and multiattribute preference models rely on algebraic utility functions or combination rules such as additive (equation 1) or multiplicative exponential utility (equation 2) functions (Timmermans 1986; Timmermans and van der Heijden 1987; Nijkamp et al. 1990; Korhonen et al. 1992).

        Recently, a number of objections against this type of evaluation modeling have been put forth (Hendriks and van der Smagt 1988; Reitsma 1990; van der Smagt and Lucardie 1991; Lucardie 1994). These objections can be summarized by the assertion that algebraic utility functions rarely adequately represent interest-specific evaluation of plans. As the various authors point out, complex conditional relationships between evaluation criteria may exist. In the simplest case, these relationships introduce conditionality (IF - THEN) into the utility functions. In the worst case, they necessitate recategorization of evaluation criteria as a function of the values that one or more other criteria take on (van der Smagt and Lucardie 1991, p.296). Other authors object to the validity of the information which serves as input for algebraic utility functions, in particular the determination of attributes and weights. They point out that many of the techniques used to collect this information, especially scaling techniques (Hourihan 1979; Burnett 1982; Rhodes and Stern 1993), cause "intrusion of the method into the results" (Pawson 1982, p. 54).
        Alternative techniques for modeling these complex types of interactions, such as decision plan nets (Timmermans and van der Heijden 1987; Op `t Veld et al. 1992) or relational reconstruction of choice sets (Reitsma 1990; Lucardie 1994) avoid some of these criticisms but they imply increased model complexity, problematic data collection and few opportunities for aggregation.
        A pragmatic way out of this morass of methodological controversy would be to not try to (re)construct people's utility functions a priori, but instead allow them to freely formulate their own; algebraic or conditional, locale-specific or spatially aggregate, weighted or non-weighted, a priori or ex-post. This, however, requires a very flexible representational scheme for utility functions and supporting data model and software.

3. Object-oriented Modeling of River Basins

Rivers and reservoir systems have been modeled as networks of discrete entities such as reservoirs, power plants, reaches, diversions, and so on (Sigvaldason 1976; Labadie and Shafer 1979; Martin 1981; Loucks et al. 1989; Palmer et al. 1993). With the recent advance of object-oriented programming techniques (Coplien 1992; Ellis and Stroustrup 1990; Lippman 1991) computer-based application of this kind of model has been greatly facilitated. Object-oriented programming provides a data model, the "object" model, which exhibits close correspondence with discrete object structures or discrete event processes. In an object-oriented model, object behavior consists of transitions from a state at time t to a state at time t+1, where the transitions are a function of the application of a dynamic D, invoked by the object itself, typically in response to a message received from another object causing a state change (equation 3).

        Applied to a river basin, river basin objects such as reservoirs, confluences, diversions, etc. are always in a particular state as expressed by the configuration of their variable and parameter state data. An object's state changes by receiving information; for example, an inflow, from another object. As a consequence, it might apply a dynamic; for example, mass balance (Chapra and Canale 1985), which further modifies its state. Certain state changes induce the object to send a message to another object. For instance, when an object computes its outflow, it may transfer that outflow to its downstream neighbor where it is received as inflow. Likewise, calculated inflows representing "requests" for water may be transferred to upstream objects. The cascading of information through the network of objects (the river system) continues until no more state changes occur. Then, the network is once again in balance, and the simulation may proceed to the next time step. Whereas previously complicated data models and control structures where necessary to manage both the state data and the dynamics, object orientation provides a data model through which both are maintained by the objects themselves rather than by an overall, system-wide control component. Similarly, rather than having some control component transfer information from one object to the next (e.g., outflow-inflow fluxes), the objects simply send and receive messages to and from each other. Although each object behaves autonomously, collectively this behavior models that of the river as a whole.
        This type of model is essentially directionless in that information "flows" between objects in the direction of the variables that need to be solved for. As such, the network structure of objects is independent of whether a model is supply- or demand driven. Which variables are solved for is exclusively a function of how the objects are equipped with data; topological sorting of the network is not necessary (Behrens 1994; Reitsma et al. 1994; Zagona et al. 1995).
        To facilitate modeling, object classes can be equipped with libraries of dynamics or methods, of which individual instances can be selected by users. For instance, reservoir objects can be equipped with several methods for computing tailwater, evaporation or elevation-area-storage relationships. Construed this way, all that is needed to construct river basin models are the following: Figure 2. Object-oriented simulation of the Colorado River between Lake Powell and Lake Mead Figure 2   Object-oriented simulation of the Colorado River between Lake Powell and Lake Mead

        Figure 2 contains an example of a simple model constructed in this manner. It represents a section of the Colorado River from Lake Powell to Lake Mead. The dark, solid objects represent the hydraulic components of the river. Water from Lake Powell is used for the generation of electricity at Glen Canyon Dam, and then flows through Glen and Grand Canyons, represented by a series of river "Reach" objects, into Lake Mead. Large scale applications of this modeling technique are presented in Zagona et al. (1995) and Eschenbach et al. (1995).

4. Object-orientated Plan Evaluation

This type of object-oriented process model can be extended to include plan evaluation. If one considers plan evaluation merely as additional processing of state data, there is no reason not to store this processing information as well as its results, on the same objects that model the physical behavior of the system. Integrating the physical process model with plan evaluation models this way generates various advantages:         In the next section, we explore the validity of the above claims through the application of the technique on the simulation and evaluation of Colorado River streamflows.

5. Case Study: Colorado River Operations

The Colorado River (Figure 3) is one of the major water resources in the Western United States. With a drainage area of approximately 632,000 square km and an average annual natural flow of 18,132 million cubic meters, the Colorado River provides water to seven states (CO, WY, UT, NM, CA, AZ and NV) as well as parts of Mexico. The section of the river from Lake Powell to Lake Mead (Figure 2), which includes Grand Canyon National Park, represents a complex management situation, with numerous users competing for and impacting

Figure 3. Colorado River Basin
Figure 3  Colorado River Basin

the same limited resource. As witnessed by the U.S. congressional mandates for this and other Colorado River projects, the initial operational objectives of Glen Canyon Dam were aimed primarily at stimulating the regional economy (water delivery and power generation) and providing protection against floods and droughts. Recently, growing pressure from an increasingly varied set of interests prompted a re-evaluation of the riverine resources and of the Glen Canyon Dam and Lake Powell management objectives. Traditional objectives such as water delivery, power generation, and flood and drought control are now considered jointly with recreational, aesthetic, and environmental needs (NRC 1987; USBR 1993).

5.1. Criteria Definitions

For this study, the model of Figure 2 was extended with a series of small models for evaluation of alternative reservoir release schedules. Five objectives, generally recognized as important considerations in operating the canyon were selected: power generation, river rafting, maintenance cost for recreational facilities, trout spawning habitat, and habitat suitability for the humpback chub, an endangered species of native fish (NRC 1987; USBR 1993). Criteria for measuring the degree to which these objectives are realized under different flow scenarios were defined in accordance with the results of a series of environmental impact studies previously conducted on the operations of Glen Canyon Dam (Bishop et al. 1988; Angradi et al. 1992; USBR 1993). For instance, to evaluate the utility of the river for white water rafting, the following empirical, second order polynomial utility function (equation 4) was derived from results presented by Bishop et al. (1988):

Y = 214.88 + 65.346X + .96126X²                                                            (4)

   Y: surplus value for commercial rafting trips in Grand Canyon National Park, and
   X: average daily flow in cubic feet per second (cfs).

The function is conditional in that only flows greater than 5,000 cfs generate utility.

        Humpback chub spawning utility was defined in accordance with the findings by Angradi et al. (1992) and USBR (1993). The utility is normalized between 0.0 and 1.0, with 1.0 describing perfect raising conditions of 5,000 cfs or lower flows, and 0.0 being a theoretical minimum under conditions of infinitely high flow. Utilities are conditional in that they are only to be computed for flows higher than 5,000 cfs, and only during critical spawning periods (July - September).

       Unlike humpback chub spawning utility, trout spawning utility varies with locale. For instance, based on the studies mentioned earlier, trout spawning utility in the Glen Canyon Reach was defined as an empirical function of the percentage of redds continuously submerged:

Y = 1 - (87,000 + 37.0X + 0.009X²) / 100                                                (5)

   Y: trout spawning suitability index.
   X: minimum daily flow in cubic feet per second (cfs).

        This utility, however, is only valid between October and May, and for flows under 15,000 cfs. Outside this period, and at flows greater than 15,000 cfs, the objective is not evaluated. For reaches other than the Glen Canyon Reach, a simple minimum flow requirement of 2,000 cfs is sufficient as at those flows trout can gain access to tributary streams where spawning can occur. Criteria for the other objectives were defined in a similar way. Note that, quite deliberately, no attempt was made to aggregate the various criteria into one, encompassing utility index. Given the plurality of objectives, the very different interests associated with them, and the legislative and political environment in which these objectives are evaluated and administered, each of the interests and their associated criteria were formulated independently and evaluated against the physical behavior of the river.

5.2. Model Specification

Table 1 summarizes the mappings of the hydrologic processes, policies and plan evaluations on the one hand and their representations in the object-oriented network model on the other.

        The hydraulic part of the model is constructed by interactively creating the various objects and linking them together into a network representing the river. Three alternative release schedules, representing typical releases under low, normal and high flow years, were specified on the Lake Powell object. These are defined as timeseries data, and loaded into the object's outflow variables. Initial conditions are represented as scalars. Parameter data (e.g., head-area-volume relationships and time lag coefficients), are represented as tables. Variables representing the evaluation functions were defined through: (a) interactive creation of additional state variables to store the evaluation results on the objects, and (b) a simple "rule" language for definition of conditional evaluation functions. This rule language follows a simple syntax:

POLICY <policy name> TO_DETERMINE <variable name>FOR <object name>
IF <conditions>

THEN <result>


For instance, the definition of humpback chub rearing utility was defined as:

POLICY chub_rearing TO_DETERMINE chub_rearing_utility FOR Reach

IF now() > June AND now() < October AND Reach.flow > 5000

THEN chub_rearing_utility = 5000/Reach.flow


Trout spawning utility for Reach1 was defined as:

POLICY trout_spawning1 TO_DETERMINE trout_spawning_utility FOR Reach1

IF now() < May AND now() > October AND Reach1.flow < 15000

THEN trout_spawning_utility = 1.0 - (87.0 + (3.7E-03) Reach1.flow -(9.0E-07)(Reach1.flow)2)/100.0


For all other reaches, trout spawning utility was defined as:

POLICY trout_spawning2 TO_DETERMINE trout_spawning_utility FOR Reach

IF now() < May AND now() > October AND Reach.flow > 2000

THEN trout_spawning_utility = 1


        The above rules for trout spawning illustrate the use of object orientation in considering the aspect of locale. Whereas trout spawning utility for Reach1 is based on a specific empirical relationship, a more general utility function is defined on the higher class level of reaches in general. Since object-specific rules take precedence over class-specific rules, the Reach1 object always applies its own trout_spawning_utility rule first. If, for any reason, all object-specific rules fail; i.e., the IF parts of the rules all evaluate to FALSE, the object attempts the application of the rules for its class. Reaches other than Reach1 only apply the more general rule. Rules for evaluating the utility for rafting as well as for the other objectives were formulated in a similar fashion. Finally, a generic output plotting object (Figure 2: "Rafting and Trout Utility Values") was defined to collect and plot all evaluation results from Reach1.
        Running the model, now equipped with several evaluation models, does not require any additional transfer of data or locational information. As the objects perform their state transitions, they now not only compute states associated with their physical processes, but also evaluate their utility functions. Physical process modeling and evaluation have been reduced to simple state transition functions under a single data model: the object.

Figure 4: Utility Values for Humpback Chub Rearing Habitat 
Figure 4  Utility Values for Humpback Chub Rearing Habitat

5.3. Results

Figure 4 presents the results for humpback chub rearing for Reach1 under the three different flow scenarios. They indicate that the mainstem Colorado River does not provide good rearing habitat under any of the three flow regimes simulated. During the critical rearing summer months, utility values vary from a low of .11 (high flow) to .40 (low flow). Apparently, under flow regimes other than a natural one where flows are well below current operational guidelines, the young chub are under stress.
Figure 5:  Utility Values for Rafting and Humpback Chub
Figure 5  Utility Values for Rafting and Humpback Chub 
        Since the evaluation functions are interest-specific and defined independently, comparing evaluation results provides insights into potential conflicts between objectives. Figure 5, for instance, shows the evaluations for both humpback chub rearing and rafting for an average flow scenario. The curves suggest that under such a scenario the rafting objective is satisfied much more closely than humpback chub rearing (both are expressed on normalized scales). The curves show a virtually perfect inverse correlation (r < -.99), indicating that what is good for the rafters is bad for the chub, and vice versa. Although this example of analysis of conflict between multiple objectives is rather simple, the concept offers interesting opportunities for more complex types of analysis. For instance, in a paper on environmental modeling and collaborative decision making, Carver et al. (1996) argue for an iterative version of this technique whereby interests are independently adjusted in search of windows of opportunity (= no conflict).


Traditional plan evaluation as part of environmental DSS often suffers from a number of problems. Algebraic utility functions are often too simple to represent the complexity of objectives and the various means of realizing them. Conditions, locale, time-specific criteria definitions and the ways in which definitions combine into an overall utility measure require flexibility in defining evaluation functions. Moreover, many environmental decision-making situations exhibit frequent changes in evaluation criteria as a function of developments in the negotiation process, internal and external politics, or new insights into how system variables are related to interests (Kaufman and Duncan 1990). Not only must DSS be flexible enough to incorporate those changes effectively, but the ad hoc nature of many of these changes necessitates efficient means for doing so. This efficiency, however, is hard to achieve using the traditional environmental DSS architecture where all three system components (state information, process information and evaluation tools) are represented by separate software and data models. For instance, to support locale-specific utility functions, as in the case of trout spawning utility, topological information about the network would have to be represented both in the simulation model and the evaluation software. Not only would this imply redundancy, but it would also mean that every time the relationship between locale and utility changes, those changes would have to be reflected in both models. The same argument can be used in favor of bringing state and process information under a single data model. Object-orientation offers attractive opportunities for developing data models which unite all three components of environmental DSS.
        Object orientation also offers attractive opportunities for interactive, ad hoc definition of utility functions. Since evaluations can be considered the results of merely processing state data such as observational data and model forecasts, both the evaluation computations and their results can be interactively expressed and stored on either the river objects or on new, data processing objects. The latter can then be linked into the network of river objects so that they will conduct their computations as part of the larger simulation. This ad hoc approach to evaluation has several advantages:         The Colorado River modeling example illustrates that object orientation allows both state and process information to be stored on objects where communication between objects represent the fluxes within the environmental system. In addition, an object that manipulates data to represent physical behavior can evaluate those same data relative to one or more predefined objectives. Hence, all three environmental DSS components reside at the object level. Locale-specific definitions are easy to create since objects or groups of objects, by definition, represent those locales. One simply attaches the appropriate evaluation rules to those locales and runs the model. Many evaluations, each representing different perspectives or "views" of an environmental resource, can be computed simultaneously, and potential conflicts in space and time can be explored by comparing the resulting utility profiles of various objectives at any number of sites.


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